I-Wen WuDr. - AOCE 2026

Dr. I-Wen Wu graduated from the College of Medicine, University of Buenos Aires, Argentina. She completed the nephrologist training at Chang Gung Memorial Hospital, Taiwan then became Clinical Professor of Nephrology at Chang Gung Memorial Hospital. She is currently the Director of the Department of Internal Medicine and Department of Medical Research at Taipei Medical University Hospital. Her research interests are focused on the area of precision medicine of CKD and multi-omics biomarkers signature. She receives research grant support from the Ministry of Science and Technology of Taiwan in the areas of uremic toxins and microbiota. In addition, Dr. Wu is devoted to promoting knowledge exchange and international collaboration between Taiwan Society of Nephrology and different academic societies worldwide, serving the role of the Director of International Affairs and Cooperation of Taiwan Society Nephrology, Deputy Chair of Diversity and Equity Committee of the Asian Pacific Society of Nephrology and member of North and East Asian Region Board of the International Society of Nephrology. She is the National Leader of various clinical trials for renal anemia, glomerulonephritis, and novel drugs focusing on renal outcomes. She has published over 120 articles in international peer-reviewed journals and written chapters for the Taiwan Renal Database System and the Taiwan CKD clinical guidelines.

22 MARCH

Time	Session

14:00 15:30	[DAROC Symposium 6] Artificial Intelligence Chung-Ze WuTaiwan Moderator Feng-Chih KuoTaiwan Moderator (TBC) Hsiu-Hsi ChenTaiwan Speaker AI-Assisted Discovery of Omic Signature in Diabetic Kidney Disease I-Wen WuTaiwan Speaker AI-Assisted Discovery of Omic Signature in Diabetic Kidney DiseaseDiabetic kidney disease (DKD) is a leading cause of chronic kidney disease and end-stage renal disease, posing significant global health and economic burdens. Traditional management of DKD relies on standardized approaches, which often fail to account for the complexity of individual patient profiles. Precision medicine leverages individualized patient data—spanning genetic, proteomic, metabolic, and clinical information—to optimize diagnosis, risk assessment, and therapeutic interventions. However, translating this paradigm into clinical practice presents significant challenges and opportunities. This presentation focuses on the practical aspects of integrating precision medicine into DKD management. Key themes include the role of genetic and epigenetic biomarkers in risk stratification, the integration of multi-omics data with machine learning for predictive modeling and the design of personalized treatment regimens using tools such as pharmacogenomics. By examining real-world implementation strategies and overcoming barriers, this presentation aims to guide healthcare providers, researchers, and policymakers toward a sustainable and patient-centered precision medicine framework for DKD. Applications of Machine Learning in Medical Research Dee PeiTaiwan Speaker The roles of Machine Learning in Medical ResearchArtificial intelligence (AI) is fundamentally transforming clinical research by democratizing access to advanced analytical tools and establishing new standards for scientific publication. This presentation outlines a comprehensive framework for integrating machine learning (ML) methodologies into clinical studies—from raw data preparation to manuscript submission—while addressing critical challenges in model development, validation, and interpretation. We emphasize that financial barriers to sophisticated analysis have largely dissolved with the advent of open-source AI platforms, enabling researchers to move beyond legacy statistical software toward reproducible, transparent ML pipelines. The workflow begins with strategic data preparation, including appropriate imputation techniques (k-NN, MissForest, MICE) and feature standardization. For binary classification tasks—common in clinical prediction—It is advocate a rigorous protocol encompassing stratified cross-validation, hyperparameter tuning via nested CV, and explicit overfitting controls (regularization, feature limitation to ≥10 events per variable). Model selection should prioritize algorithms matching the clinical question: logistic regression for interpretability, random forests for robust baselines, and gradient boosting for maximal performance—while acknowledging trade-offs in complexity and calibration risk. Evaluation must extend beyond conventional ROC-AUC to include precision-recall curves (especially for imbalanced data), calibration assessment (slope ~1, intercept ~0), Brier score, and decision curve analysis for clinical utility. SHAP values provide essential interpretability for "black-box" models, translating complex predictions into clinically actionable insights. Crucially, it is stressed that accuracy alone is misleading in medical contexts; minimizing false negatives often carries greater clinical consequence than overall accuracy. Reproducibility demands fixed random seeds, complete pipeline documentation, and packaging preprocessing steps with final models for deployment. As journals increasingly expect ML-enhanced analyses, studies relying solely on traditional statistics face diminished publication prospects. It is concluded that AI integration is no longer optional but essential for contemporary clinical research seeking impact, rigor, and real-world applicability in an era where algorithmic insight complements—not replaces—clinical expertise. 3F Banquet Hall

Time

Session

14:00

15:30

[DAROC Symposium 6] Artificial Intelligence

Chung-Ze WuTaiwan Moderator

Feng-Chih KuoTaiwan Moderator

(TBC)

Hsiu-Hsi ChenTaiwan Speaker
AI-Assisted Discovery of Omic Signature in Diabetic Kidney Disease

I-Wen WuTaiwan Speaker AI-Assisted Discovery of Omic Signature in Diabetic Kidney DiseaseDiabetic kidney disease (DKD) is a leading cause of chronic kidney disease and end-stage renal disease, posing significant global health and economic burdens. Traditional management of DKD relies on standardized approaches, which often fail to account for the complexity of individual patient profiles. Precision medicine leverages individualized patient data—spanning genetic, proteomic, metabolic, and clinical information—to optimize diagnosis, risk assessment, and therapeutic interventions. However, translating this paradigm into clinical practice presents significant challenges and opportunities. This presentation focuses on the practical aspects of integrating precision medicine into DKD management. Key themes include the role of genetic and epigenetic biomarkers in risk stratification, the integration of multi-omics data with machine learning for predictive modeling and the design of personalized treatment regimens using tools such as pharmacogenomics. By examining real-world implementation strategies and overcoming barriers, this presentation aims to guide healthcare providers, researchers, and policymakers toward a sustainable and patient-centered precision medicine framework for DKD.
Applications of Machine Learning in Medical Research

Dee PeiTaiwan Speaker The roles of Machine Learning in Medical ResearchArtificial intelligence (AI) is fundamentally transforming clinical research by democratizing access to advanced analytical tools and establishing new standards for scientific publication. This presentation outlines a comprehensive framework for integrating machine learning (ML) methodologies into clinical studies—from raw data preparation to manuscript submission—while addressing critical challenges in model development, validation, and interpretation. We emphasize that financial barriers to sophisticated analysis have largely dissolved with the advent of open-source AI platforms, enabling researchers to move beyond legacy statistical software toward reproducible, transparent ML pipelines. The workflow begins with strategic data preparation, including appropriate imputation techniques (k-NN, MissForest, MICE) and feature standardization. For binary classification tasks—common in clinical prediction—It is advocate a rigorous protocol encompassing stratified cross-validation, hyperparameter tuning via nested CV, and explicit overfitting controls (regularization, feature limitation to ≥10 events per variable). Model selection should prioritize algorithms matching the clinical question: logistic regression for interpretability, random forests for robust baselines, and gradient boosting for maximal performance—while acknowledging trade-offs in complexity and calibration risk. Evaluation must extend beyond conventional ROC-AUC to include precision-recall curves (especially for imbalanced data), calibration assessment (slope ~1, intercept ~0), Brier score, and decision curve analysis for clinical utility. SHAP values provide essential interpretability for "black-box" models, translating complex predictions into clinically actionable insights. Crucially, it is stressed that accuracy alone is misleading in medical contexts; minimizing false negatives often carries greater clinical consequence than overall accuracy. Reproducibility demands fixed random seeds, complete pipeline documentation, and packaging preprocessing steps with final models for deployment. As journals increasingly expect ML-enhanced analyses, studies relying solely on traditional statistics face diminished publication prospects. It is concluded that AI integration is no longer optional but essential for contemporary clinical research seeking impact, rigor, and real-world applicability in an era where algorithmic insight complements—not replaces—clinical expertise.

3F Banquet Hall

I-Wen WuDr. Taiwan

22 MARCH